A mobile device positioning method and a mobile device positioning apparatus (900) are disclosed. Perception vector elements (301 and 302) are associated with projection vector elements (303 and 304) based on horizontal positions of the perception vector elements (301 and 302) extracted from an image and horizontal positions of the projection vector elements (303 and 304) obtained through projection of vector elements in a map. In this way, when the horizontal positions of the associated perception vector elements (301 and 302) and the horizontal positions of the associated projection vector elements (303 and 304) are separately arranged in a same order, horizontal position distribution of all the arranged perception vector elements (301 and 302) is consistent with that of corresponding projection vector elements (303 and 304) in the arranged projection vector elements (303 and 304), and pose estimation is performed on a mobile device based on an association result.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a driving scenario machine learning network and providing a simulated driving environment. One of the operations is performed by receiving video data that includes multiple video frames depicting an aerial view of vehicles moving about an area. The video data is processed and driving scenario data is generated which includes information about the dynamic objects identified in the video. A machine learning network is trained using the generated driving scenario data. A 3-dimensional simulated environment is provided which is configured to allow an autonomous vehicle to interact with one or more of the dynamic objects.

A method of controlling movement of multi-vehicle includes acquiring a constraint condition under which vehicles move and a calculation cycle for calculating movement routes of the vehicles; acquiring a position of each vehicle; specifying a target position for each vehicle; calculating, based on the position of each vehicle, the target position, and the constraint condition, a movement route for prediction steps of each vehicle; determining, based on the movement routes of the vehicles, a driving condition of each vehicle from a current time to a unit time; and controlling movement of each vehicle. Calculating the movement route including performing optimization calculation based on an evaluation function, evaluation of which becomes higher as a deviation between the vehicle and the target position for each prediction step becomes smaller, and the constraint condition, to calculate the movement route.

Systems and methods of adjusting zone associated risks of a coverage zone covered by one or more sensors of an autonomous driving vehicle (ADV) operating in real-time are disclosed. As an example, the method includes defining a performance limit detection window associated with a first sensor based on a mean time between failure (MTBF) lower limit of the first sensor and a MTBF upper limit of the first sensor. The method further includes determining whether an operating time of the ADV operating in autonomous driving (AD) mode is within the performance limit detection window associated with the first sensor. The method further includes in response to determining that the operating time of the ADV operating in AD mode is within the performance limit detection window of the first sensor, adjusting a zone associated risk of the coverage zone to a performance risk of a second sensor.

An apparatus for controlling backward driving of a vehicle including: a driving trajectory generation unit configured to generate a driving trajectory for backward driving of an ego vehicle on a target path, using sensing information acquired while the ego vehicle drives forward along the target path; and a control unit configured to control the backward driving of the ego vehicle on the target path according to the driving trajectory generated by the driving trajectory generation unit, correct the driving trajectory using driving information of another vehicle, which has driven backward on the target path before the ego vehicle, when a change on the target path is sensed in comparison to during the forward driving of the ego vehicle during the process of controlling the backward driving of the ego vehicle, and control the backward driving of the ego vehicle according to the corrected driving trajectory.

Provided are methods for vehicle state estimation based on sensor data, which can include receiving the sensor data generated by one or more sensors, calculating a cornering stiffness value associated with the vehicle, predicting a lateral velocity value associated with the vehicle based on the cornering stiffness value, and outputting a set of vehicle state variables indicative of a current state of the vehicle at least by inputting the lateral velocity value into a recursive filter. Some methods described also include updating the cornering stiffness value based on the set of vehicle state variables, updating the lateral velocity value based on the updated cornering stiffness value, and updating the set of vehicle state variables based on the updated lateral velocity value. Systems and computer program products are also provided.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: predict, at a trained machine learning classifier, a target slip value based on a predicted slip slope and a predicted road texture, wherein the predicted slip slope and the predicted road texture are determined using sensor data representing tire forces and modify at least one vehicle action based on the target slip value when a confidence level value corresponding to the target slip value is greater than or equal to a confidence level threshold.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: estimate a slip condition corresponding to at least one vehicle wheel; and generate, via an explicit Nonlinear Model Predictive Control (NMPC) module, control data for operating the at least one vehicle wheel based on the estimated slip condition. The explicit Nonlinear Model Predictive Control (NMPC) module includes a feedforward control module that is configured to generate adjustment data based on the estimated slip condition, wherein the adjustment data modifies the control data.

Disclosed are a device for controlling a hybrid vehicle and a method thereof. The device includes a communication device that receives a plurality of data sets including a driving pattern and a control coefficient, and a controller that extracts speeds from the driving pattern, learns a control coefficient prediction model by using an average and a standard deviation of the speeds, and determines a control coefficient of the hybrid vehicle based on the control coefficient prediction model for which the learning is completed.

A method of controlling a primary vehicle (18) comprising an automated driving system (20) for driving the primary vehicle autonomously when the primary vehicle is in an autonomous mode, the primary vehicle also being operable manually by a driver when in a manual mode, the method comprising: determining failure of the driver to accept a request to switch the primary vehicle to the manual mode when the vehicle is in the autonomous mode; determining a primary vehicle driving state; acquiring vehicle data for one or more surrounding secondary vehicles (22); determining a contingency action to take with the primary vehicle based on the primary vehicle driving state and the vehicle data for the or each secondary vehicle; and outputting the contingency action to at least one system of the primary vehicle to drive the primary vehicle autonomously in accordance with the determined contingency action.